Abstract
Neural Radiance Field (NeRF) has recently demonstrated impressive photo-realistic renderings when trained on dense input views. However, it suffers from performance degradation from sparse inputs due to overfitting and inaccurate scene geometry estimation. This work addresses those challenges in few-shot neural radiance fields. Specifically, we propose initializing the training with pseudo-views augmented by forward warping using sparse inputs and Structure from Motion (COLMAP) obtained depth to prevent over-fitting at the start of training. Then, we take inspiration from classical image-based rendering techniques, and aggregate and blend features extracted from training views to supervise the distribution of rendered novel views. Furthermore, our method introduces a novel regularization by modeling the distribution of color and density. Our proposed approach outperforms baselines in the standard benchmark (e.g. DTU dataset [1]), achieving state-of-the-art performance.